Associations between osteosarcopenia and cognitive function in community-dwelling older adults: evidence from a US cohort
Bibliographic record
Abstract
BACKGROUND: This study investigated domain-specific associations between osteosarcopenia, defined as the coexistence of osteopenia or osteoporosis with low handgrip strength (HGS), and cognitive function in a cohort of older adults (≥65 years) from the 2011 to 2014 cycles of the National Health and Nutrition Examination Survey (NHANES). METHODS: Osteosarcopenia was defined by the presence of osteopenia or osteoporosis (based on femoral T-scores) combined with low HGS. Cognitive function was assessed using the Digit Symbol Substitution, Delayed Recall, Intrusion Word Count, and Animal Fluency tests. Linear regression models examined the bidirectional associations between osteosarcopenia and cognitive performance. RESULTS: The sample included 1355 older adults (mean age 70.3 ± 6.9 years; 57% women). Compared to participants with osteoporosis alone, those with coexisting osteoporosis and low HGS performed significantly worse on the Digit Symbol Test (β = -9.6; 95% CI, -16.7 to -2.5; p = .01) and had similar Delayed Recall scores (β = -0.6; 95% CI, -1.3 to 0.1; p = .10). In participants with osteopenia and low HGS, a significant association was observed only for the Digit Symbol Test (β = -8.1; 95% CI, -13.4 to -2.7; p < .01). No significant associations were found for osteoporosis or osteopenia in isolation. CONCLUSIONS: Osteosarcopenia, particularly the combination of reduced bone mineral density and low muscle strength, is associated with poorer performance in selected cognitive domains, especially processing speed and memory. These findings underscore the potential value of integrated screening approaches and multidimensional interventions targeting musculoskeletal and cognitive health in aging populations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".